Factors influencing the perception of non-familiar spectrally rotated speech syllables
Bibliographic record
Abstract
The effects of acoustic features and native phonology on the naïve perception of unfamiliar spectrally-rotated speech syllables were evaluated. Spectrally-rotated syllables were created by rotating the spectrum of naturally produced Italian CV syllables around 1500 Hz resulting in changes in the energy distribution of harmonics while preserving the temporal relationships of spectral trajectories. Italian-speaking participants with no previous spectrally-rotated speech experience were asked to describe spectrally-rotated syllables phonologically by typing onomatopoetic versions of their perceptions on a computer keyboard. The results, also used to estimate the phonological similarity of the rotated sounds, showed that the identification of rotated vowels was related to their location on the F1-F2-F3 map of native (unrotated) vowels. The discriminability of rotated sounds was examined using a separate, naïve subject group using an ABX procedure. Discrimination errors were positively correlated with the estimated phonological similarity between rotated sounds. For rotated vowels, discrimination errors were also related to their Euclidean distance in F1-F2-F3 space as revealed by regression analysis. We conclude that naïve perception of unfamiliar spectrally-rotated syllables is under the influence of native phonology, and that spectrally-rotated vowels are represented by their location in the formant space. a)Also at Behavioral Neurosciences Program, McGill University, Montreal, Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".